Landslides are natural and often quasi-normal threats that destroy natural resources and may lead to a persistent loss of human life. Therefore, the preparation of landslide susceptibility maps is necessary in order to mitigate harmful effects. The key objective of this research is to develop landslide susceptibility maps for the Taleghan basin of Alborz province, Iran, using hybrid Machine Learning (ML) algorithms, i.e., k-fold cross validation and ML techniques of credal decision tree (CDT), Alternative Decision Tree (ADTree), and their ensemble method (CDT-ADTree), which have been state-of-the-art soft computing techniques rarely used in the case of landslide susceptibility assessments. In this study, 22 key landslide causative factors (LCFs) were considered to explore their spatial relationship to landslides, based on local geomorphological and geo-environmental influences. The Random Forest (RF) algorithm was used for the identification of variables importance of different LCFs that are more prone to landslide susceptibility. A receiver operation characteristics (ROC) curve with area under the curve (AUC), accuracy, precision, and robustness index was used to evaluate and compare landslide susceptibility models. The output of the model performance shows that the CDT-ADTree model is the more robust model for the landslide susceptibility where the AUC, accuracy, and precision are 0.981, 0.837, and 0.867, respectively, than the standalone model of CDT and ADTree model. Therefore, it is concluded that the CDT-ADTree ensemble model can be applied as a new promising technique for spatial prediction of the landslide in further studies.

Application of TOPSIS method in evaluation and prioritization of social stability in rural areas (Case Study: Zidasht Basin)

Proper implementation of the participatory projects to conserve national natural resources has become much more important over the recent decades. This socio-economic research seeks developing effective strategies to increase public participation in experts' opinions of Chaharmahal and Bakhtiari Province (Iran), in order to achieve sustainable and integrated management of natural resources. In the first step, the literature review led to the identification of 57 social parameters influencing public participation in the province. In the second step, from the parameters identified, with the help of the Delphi technique, 15 parameters were finalized by experts. This led to the formulation of the effective socioeconomic strategies in the study area. The next step was to prioritize these parameters. For this purpose, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used in Topsis solver software. The study’s statistical population was comprised of the executive experts of Chaharmahal and Bakhtiari Province. The results of the prioritization indicated that the strategy of "the development of alternative livelihoods (A6)" ranks first with normal weight of 0.31, followed by "focus on profitable projects (A2)" with the normal weight of 0.2, and "strengthening social cohesion and trust (A7 )" with the normal weight of 0.12, as the most important strategies to increase public participation. The results of this study can be used by managers and executive decision-makers to protect natural resources and increase feasibility of management activities in Chaharmahal and Bakhtiari Province. Keywords: Strategies effective on public participation, Delphi technique, Executive experts, TOPSIS, Chaharmahal and Bakhtiari Province